Memory Controller Victim Map Cache Line Selection
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Solution Overview
Problem
Existing memory controllers face performance deterioration due to frequent cache misses, which occur when the physical address corresponding to a logical address is not found in the map cache area, leading to inefficient map data loading operations.
Innovation Solution
A memory controller with a map cache area that uses a victim map cache line selection model trained by reinforcement learning to select and remove unused map cache lines, minimizing the need for map data loading by replacing missing map cache lines with new ones from the memory device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a map cache area is used to store mapping information, then address translation speed is improved, but cache miss frequency increases when physical addresses are not found
Solution Approach 1:
The patent implements dynamic victim cache line selection using reinforcement learning. The system continuously learns from cache access patterns and dynamically adjusts which cache lines to evict, transitioning from static eviction policies to adaptive dynamic selection that responds to changing workload characteristics
Solution Approach 2:
The reinforcement learning mechanism establishes a feedback loop where the system monitors cache performance metrics, learns from the outcomes of eviction decisions, and continuously improves its victim selection strategy. The reward signal based on cache hit rate provides feedback that guides future eviction decisions
2Reliability
If frequent map data loading operations are performed to handle cache misses, then cache hit rate is improved, but system performance deteriorates due to operation overhead
Solution Approach 1:
The system performs preliminary actions by proactively selecting victim cache lines based on learned access patterns before cache misses occur. The reinforcement learning model anticipates which cache lines are likely to be evicted and prepares accordingly, reducing the need for frequent map data loading operations
Solution Approach 2:
The system serves itself by using reinforcement learning to autonomously optimize its own cache management. The model learns from its own performance feedback and automatically adjusts eviction policies without external intervention, minimizing the overhead of manual cache management and reducing unnecessary map data loading
3Device complexity
If simple eviction policies are used to select victim cache lines, then device complexity is reduced, but map data loading frequency increases
Solution Approach 1:
The patent replaces traditional mechanical eviction policies (such as LRU, FIFO, or random selection) with an intelligent reinforcement learning system. This substitution transitions from rule-based mechanical decision-making to learning-based adaptive decision-making, achieving better cache performance without proportionally increasing hardware complexity
Data Source
AI summary
A memory controller includes: a map cache area for storing a map cache lines including mapping information between a logical address and a physical address; a victim map cache line selector for selecting a victim map cache line among the map cache lines, using a victim map cache line selection model trained by using a storage state information as training data, when a physical address corresponding to a logical address of an operation request is absent in the map cache area; and a map data controller for removing the selected victim map cache line from the map cache area, providing the removed victim map cache line to a memory device, receiving a target map cache line including the physical address corresponding to the logical address of the operation request from the memory device, and storing the target map cache line in the map cache area.